Verification and enhancement using detection systems located at the network periphery and endpoint devices

ABSTRACT

Computerized techniques to determine and verify maliciousness of an object are described. A malware detection system intercepts in-bound network traffic at a periphery of a network to capture and analyze behaviors of content of network traffic monitored during execution in a virtual machine. One or more endpoint devices on the network also monitor for behaviors during normal processing. Correlation of the behaviors captured by the malware detection system and the one or more endpoint devices may verify a classification by the malware detection system of maliciousness of the content. The malware detection system may communicate with the one or more endpoint devices to influence detection and reporting of behaviors by those device(s).

RELATED APPLICATION

The present application claims priority from commonly owned Provisional Patent Application No. 62/316,390, entitled MALWARE DETECTION VERIFICATION AND ENHANCEMENT USING DETECTION SYSTEMS LOCATED AT THE NETWORK PERIPHERY AND ENDPOINT DEVICES, by Ashar Aziz et al., filed on Mar. 31, 2016, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to cyber security and more particularly to verifying and enhancing detection of a cyber-attack on a network.

BACKGROUND OF THE INVENTION

A cyber-attack may employ malware (malicious software), which may include a computer program or file that is harmful to a computer, computer network, and/or user. Conventional antivirus applications may be employed at computers, such as, for example, laptops and servers connectable as nodes (e.g., endpoints) of a network, to identify viruses and other malware using a signature-based approach. Antivirus applications identify malware using an antivirus engine that compares the contents of a file to a database of known malware signatures. Advanced malware often avoids detection by antivirus applications. Advanced malware is often polymorphic in nature, that is, changes its “fingerprint” while maintaining its central malicious functionality, thus avoiding matches against the signature database. Also, advanced malware is often custom-designed for use against targeted users, organizations or industries and not re-used against other targets. As such, targeted malware will often not match signatures of known generic malware. Given that advanced malware is able to circumvent conventional anti-virus analysis, this approach has been determined to be wholly deficient.

Another solution employs a malware detection system to identify malware at the network periphery. In some solutions, detection at the network periphery may utilize a conventional network intrusion detection system (IDS) often incorporated into network firewalls to compare signatures of known malware against traffic for matches while, in other solutions, a two-phase network security appliance (NSA) may be employed. The two-phase approach may compare in-bound network traffic against known characteristics of malware in a static analysis phase and identify malicious behaviors during execution of the content in a dynamic analysis phase.

Detection at the network periphery may be limited by the capability of the malware detection system for precise and effective detection without excessive false positives (wrongly identified attacks) on the one hand (such as is often the case with IDSs), and for timely analysis of behaviors of the network traffic to completely prevent network intrusion on the other (such as may be the case with some NSAs under certain circumstances). Furthermore, the analysis at the network periphery may not provide sufficient information about the particular target or targets (e.g., endpoints) within the network and the potential scope and severity of the attack.

Moreover, the proliferation of malware detection systems and security software has inundated network administrators with security alerts. Actionable intelligence may be buried within these security alerts; however, the sheer number of the alerts makes it difficult for network administrators to identify high priority alerts, a situation exacerbated by the presence of false positives. Moreover, the alerts may not contain sufficient information regarding the progression of the attack once inside the network. Accordingly, a network manager may be unable to identify whether a cyber-attack is in progress or has even occurred and to determine appropriate and timely actions to contain and remediate potential damage.

SUMMARY OF EMBODIMENTS OF THE INVENTION

Aspects of the invention reside in the interoperation of malware detection capabilities of a first network electronic device located for detection at a network periphery and a second network electronic device located for detection at a network node or endpoint device (an “endpoint device”) to verify the presence of a cyber-attack and otherwise to enhance malware detection. The first network electronic device may be embodied as a malware detection system (MDS), for example, an NSA, which receives inbound network traffic (or at least objects contained within the traffic) at the network periphery for analysis to identify those objects containing malware and thereby constituting cyber-attacks. For this purpose, embodiments of the MDS may include an object capture device at or near the periphery of the network and an object analysis component (locally or remotely situated, e.g., in the cloud) for detecting features (e.g., characteristics and/or behaviors) associated with malware. Analysis results of the MDS may be communicated to the endpoint device to guide or influence malware detection on-board the endpoint device. The results of malware detection by the MDS and the endpoint device (the respective “analysis results”) may be correlated to verify and enhance malware detection.

Correlation of the respective analysis results and classification of the object as malicious or benign may be performed by a security logic engine (“SLE”). This SLE may be implemented for example as part of the MDS or in a separate network device such as, for example, a security management console or system. More specifically, the SLE may verify a cyber-attack by synthesizing (combining) the analysis results of the MDS with the analysis results of at least one endpoint device though in many cases with a possibly large group of endpoint devices. The synthesis of the analysis results may enhance the detection of the cyber-attack by identifying further information of the attack.

For example, if an MDS operating at the periphery of a network determines an object contained in network traffic entering the network is likely malicious by analyzing the traffic and in particular the object, the MDS may communicate the results of the analysis to a SLE. In an embodiment, the MDS may utilize a static analysis logic (operable without executing the object) and a dynamic analysis logic (operable to perform behavioral analysis that involves executing the object) to generate analysis results. The SLE (which may be a component of the MDS in some embodiments) may combine the results of the static and dynamic analyses to classify the object as malicious or benign. In addition, to verify the cyber-attack identified by the MDS, an endpoint device (e.g., the endpoint device receiving the network traffic containing malicious object) may monitor the processing of the object during execution to identify characteristics and behaviors to enhance the MDS/SLE determination of maliciousness.

The endpoint device may be configured to monitor the behaviors of objects processed by the device with a view to identifying anomalous behaviors which may be indicative of malware. In an embodiment, the endpoint device processes, e.g., loads the object or otherwise executes the object during its normal operation (e.g. user-initiated processing of a document file, etc.). The endpoint device may collect “events,” that is, information regarding behaviors monitored during processing, and may communicate the events to a SLE to enhance cyber-attack detection. In some embodiments, the endpoint device may include a software agent that monitors the events and, in some embodiments, determines whether the events represent anomalous behaviors. Anomalous behaviors may include unexpected or undesired behaviors (e.g., file accesses, system resource accesses) or unwanted operations that would be acceptable in another context (e.g., locking access to the endpoint device). The endpoint device may communicate the events or just the anomalous behavioral information to the SLE logic for further correlation and classification of the object.

The SLE logic receives analysis results from the MDS and from one or more endpoint devices in the network to verify a cyber-attack by correlating the analysis results from the MDS against those of the one or more endpoint devices. The correlation may result in a threat score or other threat level of maliciousness. Where the correlation score or level exceeds a prescribed first threshold, the determination by the MDS of the object as malware is verified. In some embodiments, the SLE may alternatively or in addition correlate the analysis results from the MDS and the one or more endpoint devices with features associated with known malware to classify the object as malware (or re-classify the object as malware, where the MDS had originally classified the object as merely suspicious) where the correlation score or indicator exceeds a prescribed second threshold, which may be the same or different than the first threshold. Where the MDS determines the malicious object is associated with a select threat level or maliciousness score that level or score may be increased or decreased based on this additional correlation with the features of one or more endpoints and/or of known malware.

In some embodiments the SLE may seek classification of the object as malware as just described, and further, in some embodiments, classify the object as being part of a family of malware based on the combined features of the analysis results of the MDS and the one or more endpoint devices by correlating those analytical results against those of known malware families. Accordingly, this SLE may label the object with the name of a known malware and/or a known family of malware. In some cases, the SLE may extend a known family to encompass an additional malware by adding the features of the object under analysis to the malware family.

In an embodiment, the SLE may combine the analytical results from the MDS and the one or more endpoint devices to obtain further information regarding the nature and extent of a cyber-attack. For this, the SLE determines whether the analysis results from the endpoint device(s) reflect additional features of an attack, additional identification of vulnerable software running on the endpoint device(s) (beyond that running for example in a detection environment on the MDS), or other additional attack context (metadata). The attack context may also include, for example, the target (data, system or human) of the attack, the ultimate purpose of the attack (e.g., data exfiltration), a timestamp indicating when a detection was made at the MDS and each endpoint device, etc. By correlating analytical results from a plurality of endpoint devices, the SLE may determine one or more of the following: hallmarks of related attacks on the network, spreading of malware (infection/compromise) from one endpoint device to another (lateral spread), patient zero (the first endpoint device infected, which may or may not be the destination of the network traffic containing the object), whether the attack represents an Advanced Persistent Threat (APT) or other kind of targeted attack, and/or network or device vulnerable to the cyber-attack. In some embodiments, the SLE may trace the spread of an infection through a network by, for example, comparing the timestamps of verified malicious objects. The compared timestamps of verified malicious objects may be mapped chronologically to identify the patient-zero.

The SLE may issue an alert, e.g., over a network, to a security administrator based on verification of a cyber-attack. The alert may include information providing an assessment of the risk posed by the cyber-attack, information regarding the features of malware involved in the cyber-attack, identification of the endpoint device or devices involved in the attack, and related information such as the software executing on the endpoint device found to be affected or vulnerable. In some embodiments, the SLE alert may enhance or modify information contained in an alert previously issued by the malware detection system and/or endpoint device based on the synthesis of the results.

In some embodiments, the MDS or SLE may communicate to an endpoint device receiving the malicious object (e.g., the endpoint device that was the original destination of the network traffic containing the object) to provide information regarding detected malware features and other attack context (including, for example, indicators of compromise (IOCs)). For example, the MDS or SLE may provide the attack context including IOCs to monitoring logic of an agent on the endpoint device to configure and improve monitoring capabilities of the endpoint device during processing of the object. The MDS or SLE may provide all or a select portion of the attack context, and may do so automatically and, in some embodiments, selectively when a threat level or maliciousness score exceeds a threshold. The configuration of the monitoring logic of the agent may enable or disable select monitoring in the endpoint device, for example, to focus detection on the features (e.g., statically identified characteristics and/or behaviors of the object) identified by the MDS or SLE. The agent may also cause priority or expedited communication to the SLE of detected suspicious or malicious behavior in response to the received attack context, e.g., rather than awaiting polling by the SLE or other periodic reporting. To that end, embodiments advantageously employ a configurable agent, e.g., an agent with multiple or adaptable monitoring functions.

In some embodiments the features collected by the MDS and endpoint device(s) may be used to determine cyber vulnerability, including vulnerability of the network and/or of specific endpoint devices within the network. To that end, embodiments may collect information regarding an attack from a group of endpoint devices, for example, distributed across the network, and correlate that information with the information from the MDS. The collection may be made directly from the endpoint devices themselves or indirectly, such as via a conventional Security Information and Event Manager (STEM) that aggregates the results from the endpoint devices. Moreover, embodiments may collect attack information from endpoint devices running the same software (software profile) as that used by a detection environment of the MDS to detect the attack, and/or may run different software to scope the range of software susceptible or vulnerable to the attack. Moreover, by correlating the collected features with known malware or known malware families, a malicious object may be classified and labeled with the malware name and/or as being part of a named malware family. The MDS or SLE may determine the software configuration(s) susceptible to the malware or malware family. The system may identify endpoint devices vulnerable to the malware or malware family and initiate or transmit an alert, for example, an email, text or other communication signal, to a security administrator referencing those endpoint devices.

In some embodiments, when a threat to the network is identified, the system may issue a special action alert to devices on the network to instigate preventive or remedial action, such as to block or terminate processing of a known malicious object. By terminating processing of the known malicious object, the system may prevent lateral propagation of the malicious object, e.g. the malicious object moving from one endpoint device to another endpoint device. To further prevent lateral propagation, the system may instruct a potentially compromised endpoint device to block select outbound communications, such as those over select network ports.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of the embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIG. 1 is a block diagram of a network environment in accordance with one or more embodiments described herein;

FIG. 2 is a block diagram of an endpoint device architecture in accordance with one or more embodiments described herein;

FIG. 3 is a block diagram of a malware detection system (MDS) in accordance with one or more embodiments described herein;

FIG. 4 is a block diagram of a security logic engine in accordance with one or more embodiments described herein;

FIG. 5 is a flowchart of an exemplary method for cyber-attack verification in accordance with one or more embodiments described herein; and

FIG. 6 is a flowchart of an exemplary method to configure improved techniques for enhanced cyber-attack detection in accordance with one or more embodiments described herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of a network environment 100 that may be advantageously used with one or more embodiments described herein. The network environment 100 may be organized as a plurality of networks, such as a public network 110 and/or a private network 120 (e.g., an organization or enterprise network). According to this embodiment, the public network 110 and the private network 120 are communicatively coupled via network interconnects 130, and provide network connectivity and communication to intermediary computing devices 140, such as network switches, routers and/or firewalls, one or more endpoint device(s) 200, a malware detection system (MDS) 300 and a security logic engine 400.

The intermediary computing devices 140 communicate by exchanging packets or messages (i.e., network traffic) according to a predefined set of protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). However, it should be noted that other protocols, such as the HyperText Transfer Protocol Secure (HTTPS) for example, may be advantageously used with the inventive aspects described herein. In the case of private network 120, the intermediary computing device 140 may include a firewall or other computing device configured to limit or block certain network traffic in an attempt to protect the endpoint devices 200 from unauthorized users and attacks. The endpoint device 200 is communicatively coupled with the security logic engine 400 by the network interconnects 130, and may provide metadata monitored and stored by the endpoint device 200 to the security logic engine 400. The malware detection system 300, security logic engine 400, and optionally one or more intermediary network device 140 are similarly connected by interconnects 130.

As illustrated in FIG. 2 in greater detail, the endpoint device 200 has physical hardware including hardware processors 210, network interface(s) 220, a memory 230, a system interconnect 270, and optionally, a user interface 290. The memory 230 may contain software comprising an operating system (OS) 240, one or more applications 265, an agent 250, event processing and filtering logic 257, and, in some embodiments, an endpoint device classifier 260. The physical hardware (e.g. hardware processors 210, network interfaces(s) 220, memory 230) may be connected for communication by the system interconnect 270, such as a bus. Generally speaking, an endpoint device 200 is a network-connected electronic device, such as a general purpose personal computer, laptop, smart phone, tablet or specialized device such as point of sale (POS) terminal and server.

The hardware processor 210 is a multipurpose, programmable device that accepts digital data as input, processes the input data according to instructions stored in its memory, and provides results as output. One example of the hardware processor 210 is an Intel® microprocessor with its associated instruction set architecture, which is used as a central processing unit (CPU) of the endpoint device 200. Alternatively, the hardware processor 210 may include another type of CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or the like.

The network device(s) 280 may include various input/output (I/O) or peripheral devices, such as a storage device, for example. One type of storage device may include a solid state drive (SSD) embodied as a flash storage device or other non-volatile, solid-state electronic device (e.g., drives based on storage class memory components). Another type of storage device may include a hard disk drive (HDD). Each network device 280 may include one or more network ports containing the mechanical, electrical and/or signaling circuitry needed to connect the endpoint device 200 to the private network 120 to thereby facilitate communications over the system network 100. To that end, the network interface(s) 220 may be configured to transmit and/or receive messages using a variety of communication protocols including, inter alia, TCP/IP and HTTPS. The terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

The memory 230 may include a plurality of locations that are addressable by the hardware processor 210 and the network interface(s) 220 for storing software (including software applications) and data structures associated with such software. The hardware processor 210 is adapted to manipulate the stored data structures as well as execute the stored software, which includes an operating system (OS) 240, one or more applications 265, an agent 250, and an endpoint device classifier 260.

The operating system (OS) 240 is software that manages hardware (e.g., hardware processors 210, network interface(s) 220, memory 230, network device(s) 280, etc.), software resources, and provides common services for computer programs, such as applications 265. For hardware functions such as input and output (I/O) and memory allocation, the operating system 240 acts as an intermediary between applications 265 and the computer hardware, although the application code is usually executed directly by the hardware and frequently makes system calls to an OS function or be interrupted by it.

The agent 250 is an executable software component configured to monitor the behavior of the applications 265 and/or operating system 240. The agent 250 may be configured to monitor (via monitoring logic 255), and store metadata (e.g., state information, memory accesses, process names, time stamp, etc.) associated with content executed at the endpoint device and/or behaviors (sometimes referred to as “events”) that may be associated with processing activity. Events are behaviors of an object that are processed by one of the user mode processes and are monitored by the agent 250. Examples of these events may include information associated with a newly created process (e.g., process identifier, time of creation, originating source for creation of the new process, etc.), information about the type and location of certain data structures, information associated with an access to certain restricted port or memory address, or the like. The agent 250 may also retrieve and communicate off the endpoint device 200 to a remote electronic device such as the SLE 400 context information such as the contents of the endpoint device's memory or hard drive, and could potentially be configured to modify the contents, such as state information. Moreover, the monitoring logic 255 may be configurable so as to enable or disable the monitoring of select behaviors, activities or processes. In some embodiments, the agent 250 may include an event processing and filtering logic 257, which, for example, applies heuristics, rules or other conditions to the monitored behaviors, to identify anomalous or unexpected behaviors. The processing and filtering logic 257, in some embodiments, may scan content being processed for matches with indicators (signatures). Also, in some embodiments, the agent 250 is configured to provide the events including the metadata to the endpoint device classifier 260 so as to classify the behaviors as suspicious or even malicious. Further information regarding an embodiment of an agent may be had with reference to U.S. Pat. No. 8,949,257 issued Feb. 3, 2015, entitled “Method and System for Collecting and Organizing Data Corresponding to an Event,” the full disclosure of which being incorporated herein by reference.

The agent 250 may receive from the security logic engine 400 and/or malware detection system 300 a communication identifying a malicious object for elevated monitoring or certain specified behaviors, activities or processes to monitor. The communication identifying the malicious object may, by way of example, include a signatures (“fingerprint”), indicators, and/or patterns or sequences of behaviors. Elevated monitoring of the suspicious object may include modifying system settings or configuring the agent 250. System setting modification may include activating additional system monitors (via the monitoring logic 255) to further observe suspicious object execution and expediting communications of detection results to the SLE.

The agent 250 may provide metadata related to the monitored behaviors to the endpoint device classifier or classification engine 260 for classification of an object, e.g., as to threat level. The threat level may be indicated by the classifier 260 in any of various ways, such as indicating the object as malicious or suspicious, where “suspicious” imports less certainty or a lower threat level than a classification of “maliciousness.” The agent 250 and classifier 260 may cooperate to analyze and classify certain observed behaviors of the object, based on monitored events, as indicative of malware. The classifier 260 may also be configured to classify the monitored behaviors as expected and unexpected/anomalous, such as memory access violations, in comparison with behaviors of known malware and known benign content as identified through the use of machine learning techniques and experiential knowledge.

In some embodiments, the agent 250 may utilize rules and heuristics to identify the anomalous behaviors of objects processed by the endpoint device 200. Examples of an anomalous behavior may include a communication-based anomaly, such as an unexpected attempt to establish a network communication, unexpected attempt to transfer data (e.g., GPS data or other location data resulting in a privacy violation, contact lists, etc.), or an anomalous behavior may include an execution anomaly, for example, an unexpected execution of computer program code, an unexpected Application Programming Interface (API) function call, an unexpected alteration of a registry key, or the like. The endpoint device monitoring rules may updated to improve the monitoring capability of the agent 250.

The endpoint device monitoring rules may be periodically or aperiodically updated, with the updates received by the agent 250 from the malware detection system 200 and/or the security logic engine 400. The update may include new or modified event monitoring rules and may set forth the behaviors to monitor. The monitoring logic 255 may be configured to implement the monitoring rules received by the endpoint device agent 250. For example, the agent 250 may be updated with new behavioral monitoring rules which may be provided to the monitoring logic 255, the monitoring logic configures the monitors with the monitoring rules received by the agent 250 for a certain running process or certain application 265, for example, to monitor for spawned additional processes. Alternatively, the behavioral monitoring rules may be received by the endpoint device 200 in response to a request from the endpoint device 200 to determine whether new rules are available, and in response, the new rules are downloaded by the endpoint device 200, provided to the agent 250, used to configure the monitoring logic 255.

In some embodiments an endpoint device 200 may include a separate user interface 290. The user interface 290 may produce a graphical or textual based representation to a user of the endpoint device 200. The user interface 290 provides the user with the ability to interact with the computer. The user interface 290 may not be present for an endpoint device that is not dedicated to a single user or does not require the interaction with a user.

Malware Detection System

Referring now to FIG. 3, the malware detection system 300 can be implemented as a network security appliance. As used herein, an appliance may be embodied as any type of general-purpose or special-purpose computer, including a dedicated computing device, adapted to implement a variety of software architectures relating to exploit and malware detection and related functionality. The term “appliance” should therefore be taken broadly to include such arrangements, in addition to any systems or subsystems configured to support such functionality, whether implemented in one or more network computing devices or other electronic devices, equipment, systems or subsystems. Generally speaking, the malware detection system 300 may be implemented as one or more network-connected electronic devices, which each includes physical hardware comprising hardware processor(s), network interface(s), a memory, a system interconnect, an optional user interface, a system interconnect, which may be arranged and organized as shown in FIG. 2. Accordingly, each of the components of the malware detection system 300 shown in FIG. 3 and described below may be implemented as one or more computer programs or modules executable on one or more processors and stored in memory.

As thus embodied, the malware detection system 300 includes a network interface(s) 310, a static analysis logic 320 comprising at least an indicator scanner 330, and a heuristics engine 335, a dynamic analysis logic 340 comprising at least a scheduler 350, a store of software profiles 355, and one or more virtual machine(s) 360, an event database and logic 362, a classifying engine 380, an indicator generator 385, and a reporting engine 390. The malware analysis may involve static, dynamic and/or an optional emulation analysis, as generally described in U.S. Pat. No. 9,223,972, the entire contents of which are incorporated by reference.

The network interface(s) 310 may receive and capture network traffic transmitted from multiple devices without appreciably affecting the performance of the private network 120 or the devices coupled to the private network 120. In one embodiment, the malware detection system 300 may capture objects contained in network traffic using the network interface(s) 310, make a copy of the objects, pass the objects to the appropriate endpoint device(s) 200 and pass the copy of the objects to the static analysis logic 320 and/or the dynamic analysis logic 340. In another embodiment, the malware detection system 300 may capture the objects using the network interface(s) 310 and pass the objects to the static analysis logic 320 and/or the dynamic analysis logic 340 for processing prior to passing the objects to the appropriate endpoint device(s) 200. In such an embodiment, sometimes called a “blocking deployment,” the objects will only be passed to the appropriate endpoint device(s) 200 (e.g., the destination of the network traffic as identified in network traffic packets) if the analysis of the objects does not indicate that the objects are associated with malicious, anomalous and/or unwanted characteristics and/or behaviors.

The network interface(s) 310 and static analysis logic 320 may be located at the periphery of the private network 120. The periphery of a private network 120 may be located at or near the interconnect(s) 130 between the private network 120 and other networks, e.g., behind a firewall (not shown) on the private network 120. For example, the network interface(s) 310 and static analysis logic 320 components of the malware detection system are located at the private network periphery while the dynamic analysis logic 340, scoring logic (scorer) 370, classifying engine (classifier) 380, indicator generator 385 and reporting engine 390 are each located on a remote server on the private network 120 or on a public network 110 connected to the private network 120 via interconnects 130. Alternatively, all of these components may be co-located at or near the periphery of the private network 120.

The static analysis logic 320 may receive the network traffic to then extract the objects and related metadata, or may receive the objects and related metadata from the network interface(s) 310 already extracted. The term “object” generally refers to a collection of data, whether in transit (e.g., over a network) or at rest (e.g., stored), often having a logical structure or organization that enables it to be classified for purposes of analysis. The static analysis logic 320 may provide the objects to the indicator scanner 330 to identify if the objects match known indicators of malware. The term “indicator” (or “signature”) designates a set of characteristics and/or behaviors exhibited by one or more malware that may or may not be unique to the malware. Thus, a match of the signature may indicate to some level of probability that an object constitutes malware. In some contexts, those of skill in the art have used the term “signature” as a unique identifier or “fingerprint.” For example, a specific malware or malware family, may be represented by an indicator which is generated, for instance, as a hash of its machine code, and that is a special sub-case for purposes of this disclosure. The indicator scanner 330 may incorporate, in memory (not separately shown), a database of known malware indicators. The database of known malware indicators may be updated by receiving through the network interface(s) 310 from the public network 110 or the private network 120, via network interconnects 130, new indicators of malware. The database of indicators may also be updated by the indicator generator 385.

The heuristics engine 335 determines characteristics of the objects and/or network traffic, such as formatting or patterns of their content, and uses such characteristics to determine a probability of maliciousness. The heuristic engine 335 applies heuristics and/or probability analysis to determine if the objects might contain or constitute malware. Heuristics engine 335 is adapted for analysis of certain portions of the network traffic under analysis (e.g., the object may include a binary file) to determine whether a portion corresponds to either: (i) a “suspicious” identifier such as either a particular Uniform Resource Locator “URL” that has previously been determined as being associated with known malware, a particular source or destination (IP or MAC) address that has previously been determined as being associated with known malware; or (ii) a particular malware pattern. The heuristics engine 335 may be adapted to perform comparisons of an object under analysis against one or more pre-stored (e.g., pre-configured and/or predetermined) attack patterns stored in memory (not shown). The heuristics engine 335 may also be adapted to identify deviations in messaging practices set forth in applicable communication protocols (e.g., HTTP, TCP, etc.) exhibited by the traffic packets containing the object, since these deviations are often characteristic of malware. A match of an identifier may indicate, to some level of probability, often well less than 100%, that an object constitutes malware. The identifiers may represent identified characteristics (features) of the potential malware. The heuristics engine 335 may include scoring logic to correlate one or more characteristics of potential malware with a score of maliciousness, the score indicating the level of suspiciousness and/or maliciousness of the object. In one embodiment, when the score is above a first threshold, the heuristic engine 335 may generate an alert that the object is malicious. When the score is greater than a second threshold but lower than the first threshold, the object may be provided to the static analysis logic and/or the dynamic analysis logic for further analysis. When the score is less than the second threshold, the threat detection system may determine no further analysis is needed (e.g., the object is benign).

For dynamic analysis, the static analysis engine 320 provides the object to the scheduler 350. The scheduler 350 is responsible for provisioning and instantiating a virtual machine to execute the object at a schedule time. In some embodiments, the heuristic module 335 transmits the metadata identifying a destination device to the scheduler 350, which can then provision a virtual machine with software (operating system (OS) and one or more applications) and other components appropriate for execution of the network data (data packets or objects), which in some cases are those associated with the destination device. In other embodiments, the scheduler 350 receives one or more data packets of the network traffic from the network interface(s) 310 and analyzes the one or more data packets to identify the destination device. A virtual machine is executable software that is configured to mimic the performance of a device (e.g., the destination device).

The scheduler 350 can configure the virtual machine to mimic the pertinent performance characteristics of a destination device. The virtual machine can be provisioned from the store of software profiles 355. In one example, the scheduler 350 configures the characteristics of the virtual machine to mimic only those features of the destination device that are affected by an object to be executed (opened, loaded, and/or executed) and analyzed. Such features of the destination device can include ports that are to receive the network data, select device drivers that are to respond to the network data and any other devices coupled to or contained within the destination device that can respond to the network data. In other embodiments, the dynamic analysis logic 340 may determine a software profile, and then configures one or more virtual machine(s) 360 with the appropriate ports and capabilities to receive and execute the network data based on that software profile. In other examples, the dynamic analysis logic 340 passes the software profile for the network data to the scheduler 350 which either selects from the store of software profiles 355 or configures the virtual machine based on that profile.

The store of software profiles 355 is configured to store virtual machine images. The store of software profiles 355 can be any storage capable of storing software. In one example, the store of software profiles 355 stores a single virtual machine image that can be configured by the scheduler 350 to mimic the performance of any destination device on the private network 120. The store of software profiles 355 can store any number of distinct virtual machine images that can be configured to simulate the performance of any destination devices when processed in one or more virtual machine(s) 360.

The processing of an object may occur within one or more virtual machine(s), which may be provisioned with one or more software profiles. The software profile may be configured in response to configuration information provided by the scheduler 350, information extracted from the metadata associated with the object, and/or a default analysis software profile. Each software profile may include a software application and/or an operating system. Each of the one or more virtual machine(s) 360 may further include one or more monitors (not separately shown), namely software components that are configured to observe, capture and report information regarding run-time behavior of an object under analysis during processing within the virtual machine. The observed and captured run-time behavior information as well as effects on the virtual machine, otherwise known as features, along with related metadata may be provided to a scoring logic 370.

The scoring logic 370 generates a score used in a decision of maliciousness by the classification engine 380. The score may be a probability value (expressed in any of various ways such as, for example, a numerical value or percent) or other indicator (quantitative or qualitative) of security risk or so-called threat level. The determination of the score of the object processed by the malware detection system 300 may be based on a correlation of each of the features identified by the static analysis logic 320 and dynamic analysis logic 340 respectively. The features are the results of different analyses of the objects. The features may include characteristics, where characteristics include information about the object that do not require execution or “running” of the object. Characteristics may include metadata associated with the object, anomalous formatting or structuring of the object. The features may also include behaviors, where behaviors include information about the object and its activities during its execution or processing. Behaviors may include, but are not limited to, attempted outbound communications over a network connection or with other processes, patterns of activity or inactivity, and/or attempts to access system resources.

The scoring logic 370 may correlate one or more characteristics and monitored behaviors (features) with a weight of maliciousness. The weight of maliciousness reflects experiential knowledge of the respective features (characteristics or monitored behaviors) with those of known malware and benign objects. For example, during processing, the dynamic analysis logic 340 may monitor several behaviors of an object processed in the one or more virtual machine(s) 360, where, during processing, the object (i) executes a program, (ii) the program identifies personally identifiable data (e.g. login information, plain-text stored passwords, credit information), (iii) the program generates and encrypts the data in a new file, (iv) the program executes a network call, and (v) sends the encrypted data via the network connection to a remote server (exfiltrates the data). Each individual event may generate an independent score, weighted by the scoring logic 370, the weight based on experiential knowledge as to the maliciousness of each associated event. The individual scores or a combined score may be provided to the classifying engine 380. Alternatively, in some embodiments, the generation of a combined score may be performed by the classifying engine 380, or the scoring logic 370 and classifying engine 380 may be combined into a single engine.

The classifying engine 380 may be configured to use the scoring information provided by scoring logic 370 to classify the object as malicious, suspicious, or benign. In one embodiment, when the score is above a first threshold, the heuristic engine 335 may generate an alert that the object is malicious. When the score is greater than a second threshold but lower than the first threshold, the object may be provided for further analysis to the static analysis logic and/or the dynamic analysis logic for further analysis. When the score is less than the second threshold, the classifying engine 380 may determine no further analysis is needed (e.g., the object is benign). The threshold of maliciousness may be fixed, modified by as security administrator, and/or modified based on network conditions (for example, if a network is experiencing anomalous network conditions, if many other clients of a similar type are under confirmed attack, etc.). The classifying engine 380 may be configured to classify the object based on the characteristics identified by the static analysis logic 320 and/or the behaviors (expected and unexpected/anomalous) monitored by the dynamic analysis logic 340. In some embodiments, the classifying engine 380 may use only the correlation information provided by the scoring logic 370. That is, a determination of whether the monitored behaviors represent expected or unexpected (anomalous) behaviors is rendered by correlating the monitored behaviors against behaviors of known malware. Results of the static analysis may also be used in the correlation and classification, e.g., by being combined with the results of the dynamic analysis to yield a combined score. In an embodiment, further static analysis and/or dynamic analysis may be performed at the MDS 300 based on the results of correlation and classification engines.

In some embodiments, the classifying engine 380 may provide objects classified as malicious to the indicator generator 385, which may then generate indicators associated with these malicious objects. Additionally, the indicator generator 385 may receive non-malicious objects to generate a suitable indicator associated with non-maliciousness. In some embodiments, the indicators may be “fingerprint” type signatures, formed as a hash of the object. Alternatively, or in addition, the indicators may include identification of observed features, including characteristics and behaviors. The indicators thus generated may be provided to the security logic engine 400 for further enhancement (e.g., with additional indication of features) using results provided by endpoint devices 200. The classifying engine 380 may alternatively bypass the indicator generator 385 if it determines that the analyzed object is not malicious. The indicators may be provided to the indicator scanner 330 for use in inspecting (by scanning) subsequently received objects. In some embodiments, the indicator generator 385 may also distribute the indicators to the endpoint devices 200 and/or the security logic engine 400.

If the malware detection system classifies the object as malicious based on a static analysis results and/or dynamic analysis results, the reporting engine 390 may signal to a network or security administrator for action by an appropriate alert. In an embodiment, the reporting engine 390 may report the indicators (“signatures”) of detected behaviors of a process/object as indicative of malware and organize those indicators as reports for distribution to the endpoint devices 200.

As noted previously, the reporting logic 390 may be configured to generate an alert for transmission external to the malware detection system 300 (e.g., to one or more other endpoint devices 200, to the security logic engine 400, and/or to a network manager). The reporting logic 390 is configured to provide reports via the network interface(s) 310. The security logic engine 400, when external to the MDS 300, e.g., may be configured to perform a management function or a management system may be provided, depending on the embodiment, e.g., to distribute the reports to other MDS within the private network, as well as to nodes within a malware detection services and/or equipment supplier network (e.g., supplier cloud infrastructure) for verification of the indicators and subsequent distribution to other malware detection system and/or among other customer networks. Illustratively, the reports distributed by the management function or system may include the entire or portions of the original indicator reports provided by the MDS 300, or may include new versions that are derived from the original reports.

Security Logic Engine

As shown in FIG. 4, an embodiment of the security logic engine 400 includes a network interface(s) 410, a formatting logic 420, a correlation engine 430, a scoring logic (scorer) 440, a classification engine 450, a labeler 460, an indicator scanner 470, a risk analyzer 480, and a reporting engine 490. Generally speaking, the security logic engine 400 may be implemented as one or more network-connected electronic devices, which include(s) physical hardware comprising hardware processor(s), network interface(s), a memory, a system interconnect, an optional user interface, a system interconnect, arranged and organized as shown in FIG. 2. Each of the logic and engine components (including those just listed above) of the security logic engine 400 may be implemented as computer programs or modules executed by one or more processors and stored in memory.

The network interface(s) 410 can be coupled to a network such as private network 120 (FIG. 1) via the network interconnects 130. The network interface(s) 410 may support communication over a conventional link, such as over an Ethernet connection, USB connection, a FireWire connection, a serial connection, a parallel connection, or an ATA connection. The communication network interface(s) 410 may also support wireless communication (e.g., 802.11 a/b/g/n or wireless USB). It will be apparent to those skilled in the art that the communication network interface(s) 410 can support many wired and wireless standards. Where the SLE 400 is implemented within the MDS 300 and thus availed of network interface(s) 310, the network interface(s) 410 may be eliminated or disabled.

In some embodiments, a formatting logic 420 receives communication data from the network interface(s) 410 (or 310 of FIG. 3) and converts the data into a standardized format to be processed by other modules in the security logic engine 400 if not already in a suitable format. In general, the formatting logic 420 obtains data in disparate formats, which may often be device specific (e.g. an intermediary network device 140, malware detection system 300, endpoint device 200, etc.) or application version specific (e.g., endpoint device agent 250 may provide a particular format in version 1.0 and a different format in version 2.0), and transforms the data into a readily consumable, common format. For example, the formatting logic 420 may transform data associated with a first endpoint device agent 250 to that of a common data format (e.g. JSON, etc.) such that any ensuing analysis and correlation may be provided using a common data format.

The correlation engine 430 may correlate the features received by the security logic engine 400 from an endpoint device 200 and the malware detection system 300 with known behaviors and characteristics of benign and malicious objects. Additionally, the correlation engine 430 may correlate the features received from the endpoint device 200 with those received from the malware detection system 300 to verify the determination of maliciousness obtained by the malware detection system 300 or determine the extent to which the features from these two vantage points (network periphery and endpoint) correlate with one another. The correlations just described in the preceding two sentences can be performed separately or in the same operation depending on the implementation, and in other embodiments one or the other may be eliminated altogether.

The results of the correlation performed by the correlation engine 430 may be provided to a scorer 440. The scorer 440 may generate a score based on each correlation of an observed feature with known behaviors and characteristics of benign and malicious objects. The classification engine 450 may utilize the scores generated by the scorer 440 to classify the object as malicious if it exceeds a threshold. The threshold may be fixed or dynamic. The maliciousness threshold may be “factory-set,” “user-set,” and/or modified based on information received via a network interface(s) 410.

The correlation engine 430 may be configured, depending on the embodiment, (a) to verify a classification of maliciousness made by the malware detection system 300, (b) to provide greater or lesser confidence that an object processed by the malware detection system 300 should be classified as malware, and/or (c) to determine whether the endpoint device 200 has received and is processing malware, and if so, whether the malware is the same as that found by the malware detection system 300. The first of these requires the correlation engine 430 to correlate at least the results of the malware detection system 300 with those of the endpoint device 200. The last of these requires that the correlation engine 430 correlate the features reported by the endpoint device 200 with those of known malware, and compare the correlation results with those obtained by the malware detection system 300.

For example, the correlation engine 430 may receive, over a communication network via network interface(s) 410, (i) a feature set (features including behaviors and, in some embodiments, characteristics) monitored by the endpoint device agent 250, and (ii) a feature set (features including behaviors and, in some embodiments, characteristics) associated with an object classified by the malware detection system as malware, and in some embodiments, the associated score or threat level determined by the MDS. The correlation engine 430 may correlate the feature sets received from the endpoint device 200 and the MDS 300 to determine whether the endpoint 200 observed the same or similar features to those monitored in the MDS 300 on which its classification decision was based, and may also correlate those feature sets with features exhibited by known malware and/or malware families. In so doing, the correlation engine 430 may apply correlation rules to determine whether the feature sets separately (or those common features of the feature sets) indicate or verify the object as malware. The correlation rules may define, among other things, patterns (such as sequences) of known malicious behaviors, and, in some embodiments, also patterns of known benign behaviors. For example, in looking at patterns, a behavior may be detected that appears benign, but when examined with other behaviors, may be indicative of malicious activity.

The scorer 440 generates a risk level or numerical score used in a decision of maliciousness by the classification engine 450. The score may be a probability value (expressed in any of various ways such as, for example, a numerical value or percent) or other indicator (quantitative or qualitative) of security risk or threat level. The determination of the risk level of the object processed by the MDS 300 and observed by the endpoint device 200 may be based on monitored events used by the correlation engine 430, inter alia, (i) the location from where the object originated (e.g., a known website compared to an unknown website), (ii) the processed object spawned a new process, and/or (iii) actions taken by received objects during processing (e.g., executable code contained in objects attempts to execute a callback). An object with an associated score (value) above a first threshold may indicate a suspicious object, i.e., an object with a certain probability of being malicious, and above a second, higher threshold may indicate that object should be classified as malware, i.e., an object with a high probability of being malicious. In some embodiments, the scorer 440 may increase or decrease a score provided by the MDS 300 or may generate its own score based on all the available features of the feature sets. For example, if the results of the correlation of monitored behaviors from the endpoint device 200 and the MPS 300 and, in some embodiments, features associated with known malware, reveal a level of similarity above a first predetermined threshold (e.g., 60% or 70%), the scorer 440 may so indicate in its score. The security logic engine 400 may classify the object as malware in response to the score generated by the scorer 440.

Accordingly, the classification engine 450 may be configured to use the correlation information provided by correlation engine 430 and the score provided by a scorer 440 to render a decision as to whether the object is malicious. Illustratively, the classification engine 450 may be configured to classify the correlation information, including monitored behaviors and characteristics, of the object relative to those of known malware and benign content. If a first probability of attack (score) is received by the security logic engine 400 from a malware detection system 300 and differs by a threshold amount or falls beyond a comparison “range” from the probability of attack (score) as determined by the classification engine 450, the security logic engine 400 may generate a second classification (the classification generated by the classification engine 450), and provide the second classification to the malware detection system 300 and report the second classification in an alert. The threshold or comparison range may be fixed, and/or based on a percent of the initial classification by the malware detection system 300.

In an embodiment, the security logic engine 400 may include a labeler 460 configured to add names of malware or malware families to indicators (signatures) of malware. The labeler 460 may define a new malware family or add the identified malware to the malware family bearing the greatest similarity to the identified malware. The similarity may be based on a correlation, conducted by the correlation engine 430 or the labeler 460, of the identified malware behaviors with a database (not shown) of malware family entries and associated behaviors. Alternatively, the association of a malware family may be implemented in a separate module. The malware detection system 300 may update the indicator scanner 330 using the enhanced indicators generated by the labeler 460. These indicators may be used internally by the indicator scanner 470 of the security logic engine 400 or distributed externally as part of indicator reports to the malware detection system (s) 300 or endpoint device(s) 200.

The indicator scanner 470 receives, authenticates, and stores malware indicators, and scans results received from the malware detection system 300 and results received from an endpoint device 200 to determine, when they match, that the object under analysis is malicious. The indicator scanner 470 may also generate enhanced indicators based on the additional information received from the endpoint device 200.

The risk analyzer 480 determines the risk of harm to private network 120 from a verified malicious object based on the results provided by the classification engine 450 and labeler 460 and the indicator scanner 470. The risk analyzer 480 may base the risk of harm on information retrieved from a database regarding named malware or malware families. More specifically, the risk analyzer 480 may receive information about the object from the classification engine 450 and/or the labeler 460, which may also provide the observed behaviors from an endpoint device 200 and a malware detection system 300 as well as a malware family names identified malware names. The risk analyzer 480 may also retrieve information from the network or be provided with information about network device properties (e.g. network location, connected users, operating system version, etc.) for use in its risk assessment. The risk analyzer 480 may also receive a classification of the malware from the classification engine 450 or the signature matcher 470. The risk analyzer 480 determines a risk to the private network 120 using experiential knowledge to correlate the information about the malicious object with the information about the network device properties. The correlation results in a risk profile for each endpoint device, which may be provided to a network administrator.

The risk analyzer 480 may identify endpoint device(s) 200 that may be affected by the cyber-attack involving the verified malicious object. The risk analyzer 480 may utilize the identified features and metadata of a verified malicious object to determine if the system configuration (where a system configuration may be characterized by its hardware and software profiles) of each endpoint device in the private network 120 is vulnerable to the attack. To determine the risk posed by the verified malicious object to each endpoint device 200, the risk analyzer 480 may correlate each feature and its metadata of the object (e.g., software profile running during processing of the object during which malicious behavior was observed) with system configuration attributes of the endpoints on the network. If an endpoint device system configuration correlates with the features and metadata of the verified malware, the risk analyzer 480 identifies the endpoint device as at risk to attack.

In some embodiments, the risk analyzer 480 may communicate to a malware detection system 300 that further analysis of the verified malicious object is necessary if the risk analyzer 480 cannot determine if the verified malicious object will behave maliciously when processed by endpoint device system configurations on the private network.

The risk analyzer 480 may issue alerts to particular network devices (e.g., endpoint devices, network storage servers being accessed by an endpoint device 200 with a verified malicious object present) to restrict access from network devices found to be correlated with a high risk and/or may issue alerts to a network or security administrator via the reporting engine 490.

The reporting engine 490 is adapted to receive information from the signature matcher 470 and the risk analyzer 480 to generate alerts that identify to a user of an endpoint device, network administrator or an expert network analyst the likelihood of verified network cyber-attack. Other additional information regarding the verified malware may optionally be included in the alerts. For example, additional reported information may partially comprise typical behaviors associated with the malware, particular classifications of endpoint devices or users that may be targeted, and/or the priority for mitigation of the malware's effects. Additionally, a user of an endpoint device that was to receive the objects and/or a network administer may be alerted to the results of the processing via alert generated by a reporting engine 490. The reporting engine 490 may also provide connected malware detection systems and endpoint devices 200, with updated information regarding malicious attacks and their correlation with particular behaviors identified by the security logic engine 400. Where the security logic engine is a component of the MDS 300, the reporting engine 490 may be eliminated or combined with reporting engine 390.

FIG. 5 is an example computerized method 500 for operating the malware threat detection verification system. The term “computerized” generally represents that operations are conducted by hardware in combination with executable software and/or firmware. The procedure 500 starts at step 505 and proceeds to step 510 where a malware detection system 300 receives network traffic directed to a destination device in the private network. The destination device may be an endpoint device 200, (e.g., a desktop computer, a network attached storage server, etc.). At step 515 the malware detection system 300 processes the received network traffic using at least the static analysis logic 320 and/or dynamic analysis logic 340 to determine if the object contains characteristics or behaviors indicative of malware. The dynamic analysis logic 340 may utilize a software profile from the software profile store 355. The software profile selected from the software profile store 355 may be chosen to mimic the original destination endpoint device or a default analysis software profile set by a security administrator. At step 520 the malware detection system 300 determines if the network traffic content (object) is malicious, based on the monitored characteristics and/or behaviors. If the processed content is determined to be benign by the malware detection system 300, the method may end at step 550. In some embodiments, if the malware detection system 300 identifies the processed network content as suspicious, it may provide its analysis results to the security logic engine 400 for further analysis and correlation with endpoint device results. If the processed content is determined to be malicious, and thus indicative of an attack, by the malware detection system 300 at step 520, the malware detection system 300 may provide its determination and associated information (e.g. a signature generated by the indicator generator 385, characteristics and/or monitored behaviors identified by the static analysis logic 320 and dynamic analysis logic 340 respectively, etc.) to the security logic engine 400.

At step 525 the security logic engine receives the determination of possible attack from the malware detection system 300, which may also include information collected by the malware detection system about the processed network content. At step 530 the security logic engine receives indicators (e.g., features) from possible attack on an endpoint device 200 as identified at the endpoint device by the associated agent 250. The endpoint device agent 250 may provide the security logic engine 400 with event information about the processing of the object by the endpoint device. The information provided to the security logic engine 400 by the endpoint device 200 may include an initial determination of maliciousness by the endpoint device classifier 260 and/or behaviors monitored by the agent 250. At step 535 the security logic engine 400 may compare the received information from the endpoint device 200 with known indicators using the indicator scanner 470. Alternatively, the security logic engine 400 may employ the correlation engine 430, scorer 440, and classification engine 450 to determine if the endpoint device monitored behaviors are malicious. If the endpoint device monitored behaviors are determined to be malicious, the risk analyzer 480 verifies the network content determined to be malicious by the malware detection system 300 is malicious.

The verification step 540 requires the security logic engine 400 to combine the features (identified characteristics and monitored behaviors of the suspicious object) detected by the malware detection system 300 with the monitored behaviors of the suspicious object on the endpoint device(s) 200. Combining the features detected by the malware detection system 300 and endpoint device(s) 200 may result in a verification of a first determination of maliciousness by the malware detection system 300. The monitored behaviors from the endpoint device(s) 200 may provide further details of a suspicious object's execution resulting from, for example, longer runtimes (than on the MDS 300), user interaction, and accessibility of particular resources. The combination of the malware detection system features and endpoint device behaviors may be performed by the correlation engine 430 to increase or decrease the correlation with maliciousness.

If the first determination of maliciousness is verified by monitoring behaviors on an endpoint device 200 with a similar software profile as that used by the malware detection system, the determination may be limited to associated software profiles. The security logic engine 400 may conduct further analysis using monitored behaviors from additional endpoint devices with different software profiles to determine if the object behaves maliciously on different software profiles.

If the malware is not verified as malicious in step 540 the procedure, then ends at step 550. If the malware is verified, the risk analyzer 480 determines the threat to the network resources (endpoint devices 200, servers, etc.) and at step 545 reports the results via the reporting engine 490. Once reported (e.g. by storing the result in the security logic engine 400, issuing an alert to a third party, issuing alerts to users or network managers, and/or issuing alerts to devices, etc.), the procedure ends at step 550.

FIG. 6 represents another exemplary flowchart of a computerized method 600 for operating a malware threat detection verification system. The exemplary method 600 starts at step 605 and proceeds to step 610 where a malware detection system 300 analyzes a received object. In step 615 the malware detection system determines if the analyzed object is malicious. As described herein, the object is received in network traffic by the malware detection system and determined to be malicious based on an analysis by the system. If the malware detection system determines the object is not malicious (e.g. benign), the method ends at step 660.

If the malware detection system determines the received object is malicious (and, in some embodiments, suspicious), it may communicate with the security logic engine 400 and/or (depending on the embodiment) with an endpoint device 200 to verify the determination of maliciousness. If malware is detected in step 615, the malware detection system 300 communicates the result to a security logic engine 400 in step 620. In step 625 the malware detection system 300 may optionally determine if particular monitoring techniques that are within its capability to influence or request may be implemented at an endpoint device 200. The determination of additional monitoring techniques to be implemented by the endpoint device may be based on features identified by the malware detection system 300 in step 610 to determine if the object is malicious.

In step 627 of the exemplary method 600, the endpoint device 200 applies the “influence” techniques, e.g., the particular monitoring techniques requested by the MDS 300. Two of these are illustrated the following two steps: In step 630 the endpoint device 200 may optionally receive an indicator representing the malicious object from the malware detection system 300. The indicator can be a hash of the object found to be malicious by the malware detection system 300, which can be used to scan objects being processed by the endpoint device 200 to determined maliciousness. Also, the indicator can be descriptive of the features found to be malicious by the malware detection system 300 and can be used to identify an object processed in the endpoint that may be the same or similar to the object analyzed and found malicious by the malware detection system 300. In step 635, the endpoint device may optionally configure the behavioral monitors to detect the behaviors of the same (or similar) object on the endpoint device 200 or the behaviors specifically identified by the latter type of “feature” indicator. Responsive to the indicators, the endpoint device 200 may enable or disable monitors or configure or customize their operation.

The behaviors monitored by the endpoint device 200 in step 635 may be communicated to a security logic engine 400 in step 640. The monitored behaviors may be communicated periodically, or aperiodically, for example, when the malicious behavior is verified or alternatively when the endpoint device 200 has completed processing the malicious object. In step 645, the security logic engine 400 may combine the results of the analysis performed by the malware detection system 300 in step 610 with the monitored behaviors from endpoint device 200. The combination of the features in step 645 may be used to generate further correlations of those features with features of known malware.

If the further correlations with known malicious features exceed a threshold, as described herein, the security logic engine 400 in step 650 may corroborate the first determination of maliciousness from step 610. If the determination cannot be corroborated, the security logic engine 400 may await further monitored behaviors to be received from the endpoint device 200. In step 655 the security event analyzing logic 400 may report the verification of maliciousness to a security administrator for remediation and/or mitigation.

In one embodiment, an exemplary alert (e.g., an object, text message, display screen image, etc.), issued by step 655, is communicated to security administrators. For example, the alert may indicate the urgency in handling one or more predicted attacks based on the maliciousness of the verified malicious object. Furthermore, the alert may comprise instructions so as to prevent one or more predicted malware attacks.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software encoded on a tangible (non-transitory) computer-readable medium (e.g., disks, electronic memory, and/or CDs) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Moreover, the embodiments or aspects thereof can be implemented in hardware, firmware, software, or a combination thereof. In the foregoing description, for example, in certain situations, terms such as “engine,” “component” and “logic” are representative of hardware, firmware and/or software that is configured to perform one or more functions. As hardware, engine (or component/logic) may include circuitry having data processing or storage functionality. Examples of such circuitry may include, but is not limited or restricted to a microprocessor, one or more processor cores, a programmable gate array, a microcontroller, an application specific integrated circuit, semiconductor memory, or combinatorial logic. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention. 

What is claimed is:
 1. A cyber security system to determine and verify a presence of a cyber-attack, comprising: a system including at least one processor to communicate over the network directly or indirectly with one or more endpoint devices, intercept in-bound network traffic at or near a periphery of the network, analyze a first object included in the network traffic to identify one or more first features associated with the first object, the one or more first features including behaviors exhibited during processing of the first object in a virtual machine, and generate a first determination of maliciousness in response to the identified one or more first features associated with the first object; at least one endpoint device targeted to receive the network traffic, the at least one endpoint device including at least one processor to identify one or more second features associated with one or more objects including a second object corresponding to the first object, wherein the one or more second features include behaviors identified during processing of the one or more objects; and a security logic engine to receive the one or more first features identified by the system and the one or more second features identified by the at least one endpoint device, verify the first determination of maliciousness of at least the first object based on a combination of the one or more first features and the one or more second features to classify the first object as malware, and generate an alert to report the classification as malware, wherein the alert includes information that identifies (i) a first software profile associated with the virtual machine in which the one or more first features are exhibited and (ii) a second software profile associated with the at least one endpoint device in which the one or more second features are exhibited during processing of a second object.
 2. The cyber security system of claim 1, wherein the system comprises a network security appliance.
 3. The cyber security system of claim 1, wherein the system comprises the security logic engine.
 4. A security logic engine to determine and verify a presence of a cyber-attack, comprising: a network interface configured to (i) receive information from a system including a first set of features associated with a first object included in the network traffic including behaviors exhibited during processing of the first object in a virtual machine and (ii) receive information from at least one endpoint device including a second set of features associated with one or more objects including a second object corresponding to the first object, wherein the one or more second features include behaviors identified during processing of the one or more objects including the second object; correlation logic to determine maliciousness of at least the first object based on a combination of the first set of features provided from the system and the second set of features to classify the first object as malware; and a reporting engine to generate an alert to report the classification as malware, wherein the alert includes information that identifies (i) a first software profile associated with the virtual machine in which the one or more first features include one or more behaviors exhibited during processing of the first object within the virtual machine and (ii) a second software profile associated with the at least one endpoint device in which the one or more second features are exhibited during processing of the second object.
 5. The security logic engine of claim 4, wherein the at least one endpoint device includes a group of endpoint devices; the network interface receiving information from each of the group of endpoint devices regarding a respective software profile running during processing of the one or more objects; the reporting engine including the information regarding the respective software profiles and risk profile of the malware as part of the alert.
 6. A computerized method to determine and verify a presence of a cyber-attack, comprising: receiving one or more first features based on an analysis of a first object that is intercepted by a system from in-bound network traffic at or near a periphery of a network, the one or more first features including behaviors exhibited during processing of the first object in a virtual machine and a first determination of maliciousness of the first object being generated based on the one or more first features; receiving one or more second features associated with a second object corresponding to the first object, wherein the one or more second features include behaviors related to processing of the second object within an endpoint device of one or more endpoint devices; generating a second determination of maliciousness based on a combination of the one or more first features and the one or more second features; classifying at least the first object as malware based on the first determination of maliciousness and the second determination of maliciousness; and generating an alert to report the classification of the first object as malware, wherein the alert includes information that identifies (i) a first software profile associated with the virtual machine in which the one or more first features exhibited the behaviors during processing of the first object and (ii) a second software profile associated with each compromised endpoint device corresponding to a set of the one or more endpoint devices in which the one or more second features were exhibited during processing of the second object.
 7. The computerized method of claim 6, wherein the second object is a copy of the first object received and processed by the system that produces the one or more first features.
 8. The computerized method of claim 7, wherein: generating the first determination of maliciousness includes generating a first indicator associated with the first object; and generating the second determination of maliciousness includes generating a second indicator associated with the second object, and classifying the first object as malware includes correlating the first indicator with the second indicator.
 9. The computerized method of claim 7, wherein the receiving of the one or more first features, the receiving of the one or more second features, the generating of the second determination of maliciousness and the classifying at least the first object as malware are conducted by a security logic engine.
 10. The computerized method of claim 9, further comprising classifying the first object as belonging to one or more known malware or malware families in response to correlating the combination of the one or more first features and the one or more second features with features associated with the one or more known malware or malware families.
 11. The computerized method of claim 6, wherein the one or more first features including behaviors exhibited during processing of the first object in a virtual machine.
 12. The computerized method of claim 6, further comprising: associating a name of a known malware or malware family with the first object classified as malware; accessing a database to identify software having vulnerabilities to the known malware or malware family; and accessing a store providing software profiles of a plurality of other endpoint devices connected for communication on the network to identify each of the other endpoint devices that may run the identified software and thus may constitute a compromised endpoint device.
 13. The computerized method of claim 6, further comprising the system communicating with at least the endpoint device of the one or more endpoint devices to provide information associated with a third object corresponding to the first object classified as malware, the system being remotely located from the one or more endpoint devices.
 14. The computerized method of claim 13, further comprising identifying, for communication with the system, at least one of the one or more endpoint devices based on a destination address included in the network traffic containing the third object corresponding to the first object classified as malware.
 15. The computerized method of claim 6, wherein each agent deployed within each of the one or more endpoint devices monitors behaviors related to the processing of a version of the first object.
 16. The computerized method of claim 15, further comprising monitoring the behaviors by each agent operating on an associated endpoint of the one or more endpoint devices; modifying the monitoring of the agents in response to the first determination.
 17. The computerized method of claim 6, further comprising configuring an endpoint monitoring logic to monitor for features based on the first determination.
 18. The computerized method of claim 7, further comprising: monitoring a pattern of behaviors at the one or more endpoint devices in response to processing the second object; preventing lateral propagation of a third object corresponding to the first object that might infect other endpoint devices on the network.
 19. The computerized method of claim 18, wherein the preventing of the lateral propagation comprises instructing at least one of the one or more endpoint devices to terminate processing of the second object.
 20. The computerized method of claim 18, wherein the preventing of the lateral propagation comprises closing at least one network port at each of the one or more compromised endpoint devices.
 21. The computerized method of claim 6, prior to receiving the one or more first features based on the analysis of the first object that is intercepted by the system, further comprising: determining a software profile based on the first object; provisioning the virtual machine with the software profile; and conducting the analysis of the first object within the virtual machine.
 22. The computerized method of claim 21, wherein prior to receiving the one or more second features associated with the second object, the method further comprising: processing the first object in virtual machine dynamic analysis logic with a software profile configured to simulate a software configuration of at least one of the one or more endpoint devices.
 23. The computerized method of claim 6, wherein each of the one or more first features constitutes a statically identified characteristic or a behavior of the first object.
 24. The computerized method of claim 6, wherein the second object corresponding to the first object as the second object is based on the first object.
 25. The computerized method of claim 6, wherein the second object corresponding to the first object by including information associated with the first object.
 26. The computerized method of claim 13, wherein the third object corresponding to the first object corresponds to the second object.
 27. The cyber security system of claim 1, wherein the second object corresponding to the first object as the second object is based on the first object.
 28. The cyber security system of claim 1, wherein the second object corresponding to the first object by including information associated with the first object.
 29. The cyber security system of claim 1, wherein the security logic engine is further configured to (i) associate a name of a known malware or malware family with the first object classified as malware, (ii) access a database to identify software having vulnerabilities to the known malware or malware family, and (iii) access a store providing software profiles of a plurality of other endpoint devices connected for communication on the network to identify each of the other endpoint devices that may run the identified software and may constitute a compromised endpoint device.
 30. The cyber security system of claim 1, wherein the security logic engine is further configured to communicate with the at least one endpoint device to provide information associated with the second object corresponding to the first object classified as malware.
 31. The cyber security system of claim 1, wherein the security logic engine is further configured to identify one or more endpoint devices, including the at least one endpoint device, based on a destination address included in the network traffic containing a third object corresponding to the first object classified as malware.
 32. The cyber security system of claim 1, wherein the at least one endpoint device includes an agent that is configured to monitor behaviors related to the processing of the second object corresponding to the first object.
 33. The cyber security system of claim 1, wherein the agent includes an endpoint monitoring logic configured to monitor for features based on the first determination generated by the system.
 34. The cyber security system of claim 1, wherein the security logic engine being configured to (i) monitor a pattern of behaviors at one or more endpoint devices including the at least one endpoint device in response to processing the second object and (ii) prevent lateral propagation of a third object corresponding to the first object or the second object that might infect endpoint devices other than the at least one endpoint device.
 35. The cyber security system of claim 34, wherein the security logic engine is configured to prevent lateral propagation comprises instructing the at least one endpoint device to terminate processing of the second object or to close at least one network port.
 36. The security logic engine of claim 4, wherein the second object corresponding to the first object as the second object is based on the first object.
 37. The security logic engine of claim 4, wherein the correlation logic is configured to determine maliciousness of the first object and verify the cyber-attack by correlating the first set of features and the second set of features with features associated with known malware.
 38. The security logic engine of claim 4, further comprising: a classifying engine configured classify the first object as malicious or benign.
 39. The security logic engine of claim 4, further comprising an indicator generator configured to generate at least an indicator associated with the first object classified as malware, the indicator including a signature or a hash of the first object.
 40. The security logic engine of claim 39 further comprising: a labeler to add a representative name of a malware or a malware family to an indicator of the malware or the malware family. 